Smart farming is a development that has emphasized information and communication technology used in machinery, equipment, and sensors in network-based hi-tech farm supervision cycles. Innovative technologies, the Internet of Things (IoT), and cloud computing are anticipated to inspire growth and initiate the use of robots and artificial intelligence in farming. Such ground-breaking deviations are unsettling current agriculture approaches, while also presenting a range of challenges. This paper investigates the tools and equipment used in applications of wireless sensors in IoT agriculture, and the anticipated challenges faced when merging technology with conventional farming activities. Furthermore, this technical knowledge is helpful to growers during crop periods from sowing to harvest; and applications in both packing and transport are also investigated.

Wireless sensor networks (WSNs) are structured for monitoring an area with distributed sensors and built-in batteries. However, most of their battery energy is consumed during the data transmission process. In recent years, several methodologies, like routing optimization, topology control, and sleep scheduling algorithms, have been introduced to improve the energy efficiency of WSNs. This study introduces a novel method based on a deep learning approach that utilizes variational autoencoders (VAEs) to improve the energy efficiency of WSNs by compressing transmission data. The VAE approach is customized in this work for compressing WSN data by retaining its important features. This is achieved by analyzing the statistical structure of the sensor data rather than providing a fixed-size latent representation. The performance of the proposed model is verified using a MATLAB simulation platform, integrating a pre-trained variational autoencoder model with openly available wireless sensor data. The performance of the proposed model is found to be satisfactory in comparison to traditional methods, like the compressed sensing technique, lightweight temporal compression, and the autoencoder, in terms of having an average compression rate of 1.5572. The WSN simulation also indicates that the VAE-incorporated architecture attains a maximum network lifetime of 1491 s and suggests that VAE could be used for compression-based transmission using WSNs, as its reconstruction rate is 0.9902, which is better than results from all the other techniques.

A dual resonant G shaped and U shaped metamaterial structure with dipole antenna is designed, fabricated on FR4 substrate and measured using Agilent network analyzer. The metamaterial based loading creates additional resonance, so this proposed antenna effectively covers both 2.4 GHz and 5.8 GHz bands. The return losses of the measured results are in agreement with simulated results, the measured S parameters and simulated radiation pattern showed that the proposed design is suitable for WLAN frequency regions. The measured return loss (S parameter) of G shaped metamaterial based dipole is−21.45 dB at 2.4 GHz and simulated return loss is −21.61 dB at 5.2 GHz. The measured return loss of U shaped metamaterial is −26.11 dB at 2.4 GHz and second simulated resonance frequency occurs at 5.8 GHz at the return loss value of −23.96 dB which is most suitable for wireless LAN application.

Coordinated Multi-Point (CoMP) activity is considered as an amazing innovation focused for LTE-A standard to diminish obstruction, improve range effectiveness, increment information throughput and upgrade viable inclusion region, specifically for cell-edge clients. Interference is a major drawback in Long Term Evolution (LTE/LTE-Advanced) framework.The paper focuses the standardization and inspiration of explicit plan decisions for composed multipoint (CoMP) support in reasonable system executions, including Fundamental activity standards. There are a few strategies accessible for CoMP innovation where most methodologies require some planning data in regards to the UEs information at the distinctive BSs that must be mutual among them. This sharing outcomes expanded unpredictability and flagging overhead on arrange.